• 융합신호처리학회논문지
• /
• 제16권2호
• /
• pp.44-49
• /
• 2015

텍스처가 부족한 장면이나 카메라 포즈 변화가 클 경우, 기존의 텍스처 기반의 특징 추적 방법의 신뢰도는 크게 떨어진다. LLAH와 같은 특징 사이의 기하 정보를 활용하는 토폴로지 기반 특징 기술 방법이 좋은 대안이 될 수 있으나, 특징 검출방법의 성능에 크게 영향을 받는다. 본 논문에서는 토폴로지 기반 특징 기술을 위한 효과적인 특징 검출 방법을 마련하기 위한 기초 연구로, OpenCV 라이브러리에서 제공되는 특징 검출 방법들의 반복성(repeatability) 분석을 통해 토폴로지 기반 특징 기술에의 적용 가능성을 살펴본다. 실험을 통해, FAST의 성능이 가장 우수함을 확인하였다.

• 한국정보통신학회:학술대회논문집
• /
• 한국정보통신학회 2015년도 춘계학술대회
• /
• pp.919-921
• /
• 2015

에지는 영상에서 물체의 인식, 검출 등의 여러 분야에서 영상을 간략화하기 위해 전처리 과정으로서 널리 활용되고 있다. 일반적으로 널리 알려진 에지 검출 방법에는 Sobel, Roberts, Laplacian 등이 있으며, 이러한 기존의 방법들은 구현이 간단한 장점을 가지나, 고정 가중치 마스크를 이용하기 때문에 다소 미흡한 결과를 나타낸다. 따라서 기존의 에지 검출 방법의 문제점을 보완하고 우수한 에지 검출 특성을 얻기 위해 화소 분포를 고려한 에지 검출 알고리즘을 제안하였다. 제안한 알고리즘을 평가하기 위해 기존의 방법과 시뮬레이션하였다.

• Journal of Dairy Science and Biotechnology
• /
• 제31권1호
• /
• pp.59-65
• /
• 2013

Antibiotic residues are undesirable in milk and milk products for a number of reasons. In particular, they can have harmful effects on public health and harm to the manufacturer of the cultured milk products, e.g. MRSA etc. Although government regulatory agencies and the dairy industry have been successful in decreasing the presence of high concentrations of antibiotic residues, violations still occur and lead to contaminated products. As a result, several rapid and reliable methods for the detection of antibiotic residues have been developed, including microbiological and instrumental analysis methods. The conventional methods are time consuming, but recent improvements have allowed for better detection time, sensitivity, and accuracy. An example of an advanced detection instrument is the biosensor, which has several applications in food and environmental science, e.g. food-born pathogen detection, antimicrobial residues etc. In the present review, the recent trends in the methods used to test for antibiotic residues in milk and dairy products, as well as their specific applications, have been discussed.

• Journal of Dairy Science and Biotechnology
• /
• 제38권4호
• /
• pp.169-188
• /
• 2020

Salmonella spp. is the most common cause of gastrointestinal food poisoning worldwide, and human salmonellosis is mostly caused by the consumption of contaminated food. Therefore, the development of rapid detection methods for Salmoenlla spp. and rapid identification of the source of infection by subtyping are important for the surveillance and monitoring of food-borne salmonellosis. Therefore, this review introduces (1) History and nomenclature of Salmoenlla spp., (2) Epidemiology of Salmoenlla spp., (3) Detection methods for Salmoenlla spp. - conventional culture method, genetic detection method, molecular detection methods, and aptamer, and (4) Subtyping methods for Salmoenlla spp. - pulsed-field gel electrophoresis and repetitive sequence-based polymerase chain reaction (PCR).

• 대한원격탐사학회:학술대회논문집
• /
• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
• /
• pp.34-37
• /
• 2005

An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.

• Smart Structures and Systems
• /
• 제11권2호
• /
• pp.217-240
• /
• 2013

In this paper, practical methods to utilize PZT's dual piezoelectric effects (i.e., dynamic strain and electro-mechanical (E/M) impedance responses) for damage detection in beam-type structures are presented. In order to achieve the objective, the following approaches are implemented. Firstly, PZT material's dual piezoelectric characteristics on dynamic strain and E/M impedance are investigated. Secondly, global vibration-based and local impedance-based methods to detect the occurrence and the location of damage are presented. Finally, the vibration-based and impedance-based damage detection methods using the dual piezoelectric responses are evaluated from experiments on a lab-scaled beam for several damage scenarios. Damage detection results from using PZT sensor are compared with those obtained from using accelerometer and electric strain gauge.

• ETRI Journal
• /
• 제41권5호
• /
• pp.560-573
• /
• 2019

Two supervised learning algorithms, a basic neural network and a long short-term memory recurrent neural network, are applied to traffic including DDoS attacks. The joint effects of preprocessing methods and hyperparameters for machine learning on performance are investigated. Values representing attack characteristics are extracted from datasets and preprocessed by two methods. Binary classification and two optimizers are used. Some hyperparameters are obtained exhaustively for fast and accurate detection, while others are fixed with constants to account for performance and data characteristics. An experiment is performed via TensorFlow on three traffic datasets. Three scenarios are considered to investigate the effects of learning former traffic on sequential traffic analysis and the effects of learning one dataset on application to another dataset, and determine whether the algorithms can be used for recent attack traffic. Experimental results show that the used preprocessing methods, neural network architectures and hyperparameters, and the optimizers are appropriate for DDoS attack detection. The obtained results provide a criterion for the detection accuracy of attacks.

• 한국식품위생안전성학회지
• /
• 제11권2호
• /
• pp.129-132
• /
• 1996

Recently, as concern about the residual antibiotics in milk increase, the detection methods of residual antibiotics used extensevely at the present time were investigated and compared to their properties and the detection limits of variable antibiotics. At first, comparactive tests of the detectable sensitivity of 4 test organisms, B. cereus, B. subtilis, M.luteus, B.stearothermophilus C-953, were performed by disc assay. As a result, B.stearothermophilus was the most sensitive strain of all other strains and showe the detect limit of 5-50 ppb for penlicillins (PCs). And also, B.subitilis was showed the more effective detection limit, 200-400 ppb, for aminoglycosides (AGs) and M.luteus was showed predominant sensitivity , 50-500 ppb for macrolides(MLs) and B.cereus was the most sensitive strain for tetracyclines (TCs) and showed the detection limit of 100-400 ppb. Therefore, each test strains were showed a different sensitivity in the detection of the different antibiotic families. When the detection limit of disc assay and other methods were compared, TTCmethod was less sensitive than other methods showing 5-50 ppb detectable lebel for PCs. Also, for the detection of other antibiotic families TTC method was showed the worst sensitivity and Delvo and Charm Farm tests were similar to the detectable properties of AGs and MLs. Although disc assay was showed the similar detection limit for PCs with Delvo and Charm Farm, it was more widely effective for the detection of kanamycin, erythromycin, chlortetracycline, doxycycline, verginiamycin and so on than Delvo or Charm Farm. CharmII test was showed the best sensitivity for the most of antibiotics except neomycin and gentamycin. But it was necessary that different tests must be performed to each antibiotic family and so it was regarded that the effectiveness of that method was low.

• 전자공학회논문지
• /
• 제52권12호
• /
• pp.150-157
• /
• 2015

보행자 인식은 차량자율주행 및 사고방지를 위해 중요한 요소기술로서 많이 연구되고 있다. 이 기술들은 크게 카메라기반과 LIDAR 기반, 두 가지로 구별할 수 있다. 카메라 기반 방법과 대비되어 LIDAR 기반 방법은 화각이 넓고 조도환경에 영향을 받지 않는다는 강점이 있다. 하지만 LIDAR 기반 방법은 먼 물체를 인식하기엔 센서 해상도가 낮고, 복잡한 환경에서는 분할 오류나 폐색 등의 원인으로 인식률이 낮아진다는 문제점이 있다. 본 논문에서는 3차원 LIDAR 기반 보행자 탐지 알고리즘의 낮은 인식률을 개선시키기 위해 다중객체추적 기법의 하나인 GM-PHD 필터를 이용한 두 가지 성능 향상 방법을 제안한다. 첫 번째 방법은 GM-PHD 필터를 이용해 이전 프레임의 포인트를 현재 프레임의 물체에 자동으로 누적하여 물체 해상도 및 보행자 분류 성능을 향상시킨다. 두 번째 방법은 인식 성능이 낮은 상황에 맞춰 개선된 GM-PHD 필터를 분류된 다중객체에 적용하여 탐지 성능을 더욱 강화시킨다. 직접 취득한 도로 주행 데이터에 두 방법을 적용하여 제안한 방법이 기존의 보행자 탐지 알고리즘 성능을 대폭 향상시키는 것을 정량적으로 증명하였다.

• 제어로봇시스템학회:학술대회논문집
• /
• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
• /
• pp.537-542
• /
• 1994

This paper gives a general survey of model-based fault detection and dignosis methods. Specific applications of these ideas to boiler systems will also be discussed. A novel aspect of the fault detection technique described here is that it explicitly accounts for the effects of using simplified models and errors from linearizing a nonlinear system at an operation point. Inclusion of these effects is shown to lead to novel fault detection procedures which outperform existing methods when applied to typical fault scenarios in boiler systems.